DISCO: Adversarial Defense with Local Implicit Functions

The problem of adversarial defenses for image classification, where the goal is to robustify a classifier against adversarial examples, is considered. Inspired by the hypothesis that these examples lie beyond the natural image manifold, a novel aDversarIal defenSe with local impliCit functiOns (DISCO) is proposed to remove adversarial perturbations by localized manifold projections. DISCO consumes an adversarial image and a query pixel location and outputs a clean RGB value at the location. It is implemented with an encoder and a local implicit module, where the former produces per-pixel deep features and the latter uses the features in the neighborhood of query pixel for predicting the clean RGB value. Extensive experiments demonstrate that both DISCO and its cascade version outperform prior defenses, regardless of whether the defense is known to the attacker. DISCO is also shown to be data and parameter efficient and to mount defenses that transfers across datasets, classifiers and attacks.Comment: Accepted to Neurips 202

ProTeCt: Prompt Tuning for Hierarchical Consistency

Large visual-language models, like CLIP, learn generalized representations and have shown promising zero-shot performance. Few-shot adaptation methods, based on prompt tuning, have also been shown to further improve performance on downstream datasets. However, these models are not hierarchically consistent. Frequently, they infer incorrect labels at coarser taxonomic class levels, even when the inference at the leaf level (original class labels) is correct. This is problematic, given their support for open set classification and, in particular, open-grained classification, where practitioners define label sets at various levels of granularity. To address this problem, we propose a prompt tuning technique to calibrate the hierarchical consistency of model predictions. A set of metrics of hierarchical consistency, the Hierarchical Consistent Accuracy (HCA) and the Mean Treecut Accuracy (MTA), are first proposed to benchmark model performance in the open-granularity setting. A prompt tuning technique, denoted as Prompt Tuning for Hierarchical Consistency (ProTeCt), is then proposed to calibrate classification across all possible label set granularities. Results show that ProTeCt can be combined with existing prompt tuning methods to significantly improve open-granularity classification performance without degradation of the original classification performance at the leaf level

Spatio-Temporal Modeling for Flash Memory Channels Using Conditional Generative Nets

We propose a data-driven approach to modeling the spatio-temporal characteristics of NAND flash memory read voltages using conditional generative networks. The learned model reconstructs read voltages from an individual memory cell based on the program levels of the cell and its surrounding cells, as well as the specified program/erase (P/E) cycling time stamp. We evaluate the model over a range of time stamps using the cell read voltage distributions, the cell level error rates, and the relative frequency of errors for patterns most susceptible to inter-cell interference (ICI) effects. We conclude that the model accurately captures the spatial and temporal features of the flash memory channel

MUC4 gene polymorphisms associate with endometriosis development and endometriosis-related infertility

<p>Abstract</p> <p>Background</p> <p>Mucin 4 (<it>MUC4</it>) plays an important role in protecting and lubricating the epithelial surface of reproductive tracts, but its role in the pathogenesis of endometriosis is largely unknown.</p> <p>Methods</p> <p>To correlate <it>MUC4 </it>polymorphism with the risk of endometriosis and endometriosis-related infertility, we performed a case-control study of 140 patients and 150 healthy women. Six unique single-nucleotide polymorphisms (SNPs) (rs882605, rs1104760, rs2688513, rs2246901, rs2258447 and rs2291652) were selected for this study. DNA fragments containing the target SNP sites were amplified by polymerase chain reaction using the TaqMan SNP Genotyping Assay System to evaluate allele frequency and distribution of genotype in <it>MUC4 </it>polymorphisms.</p> <p>Results</p> <p>Both the T/G genotype of rs882605 and the frequency of haplotype T-T (rs882605 and rs1104760) were higher in patients than in controls and were statistically significant. The frequency of the C allele at rs1104760, the C allele at rs2688513, the G allele at rs2246901 and the A allele at rs2258447 were associated with advanced stage of endometriosis. Moreover, the G allele at rs882605 was verified as a key genetic factor for infertility in patients. Protein sequence analysis indicated that amino acid substitutions by genetic variations at rs882605, rs2688513 and rs2246901 occur in the putative functional loops and the type D von Willebrand factor (VWFD) domain in the MUC4 sequence.</p> <p>Conclusions</p> <p><it>MUC4 </it>polymorphisms are associated with endometriosis development and endometriosis-related infertility in the Taiwanese population.</p